A dataset of high-resolution digital elevation models of the Skeiðarársandur kettle holes, Southern Iceland

In studies of the relief evolution of smaller landforms, up to several dozen meters in width/diameter, digital elevation models (DEMs) freely accessible in different repositories may be insufficient in terms of resolution. Existing geophysical or photogrammetric equipment is not always available due to costs, conditions and regulations, especially for students or young researchers. An alternative may be the handy-held ground-based Structure from Motion technique. It allows us to obtain free high-resolution DEMs (~0.05 m) using open-source software. The method was tested on kettle holes of the glacial flood origin on Skeiðarársandur (S Iceland). The material was collected in 2022 at two outwash levels of different ages and vegetation cover. The dataset is available in the Zenodo repository; the first part is data processed in point clouds and DEMs, and the second includes original videos in MOV format. The data can be used as a reference to assess changes in the kettle hole relief in subsequent research seasons, as a methodological study for other projects, or for didactic purposes.

Various platforms are used to obtain SfM photos, from masts or poles, through blimps, fixed-wing unmanned aerial systems (UAVs)/multicopters, kites to heli-/gyrocopters and light aircraft 1 .The use of UAVs is of particular interest 10,13,17,19,22,24,26,40,44,[46][47][48] .However, most of the mentioned methods are still relatively expensive, and operation depends on, for example, battery life, wind conditions, permits, and authorizations.We can witness interesting, dynamic processes, but we do not always have access to expensive equipment, and the weather is not always favourable for carrying out research using them.The solution in such a situation, especially for small landforms or objects, may be using the ground-based SfM method.Although limited by image swath 1 , it is cost-free and gives satisfactory accuracy, guaranteeing promising future DEM resolution of centimetres.Research costs may be an essential issue for young researchers or students.
The object of the study was closed depressions of glacial flood origin, called kettle holes, located on Skeiðarársandur in S Iceland, the most extensive European active outwash plain (Fig. 1).Their formation was related to the disintegration of buried glacial ice, detached from the glacier front when meltwaters burst into the forefield 41,[49][50][51][52][53][54][55][56] .Further shaping depends on processes related to mass movements, water runoff, aeolian sedimentation and denudation, processes of colonization and plant succession.
The study aimed to obtain a database of DEMs for kettle holes using the SfM technique.The following conditions for obtaining data were adopted: (1) use of an entry-level digital single-lens reflex camera with a recording function (ground-based, hand-held technique), (2) scaling of the point cloud using simple field measurements based on the location of 4 up to 6 wooden stakes, (3) use of free software, mainly under the GUI license, (4) development of DEMs with a resolution of at least 0.05 m × 0.05 m, (5) use of an open repository for the prepared data set.The research aimed to simplify the measurements as much as possible and minimize costs while maintaining the high accuracy of the acquired data.After subsequent measurement seasons, the obtained material will be used as a reference to examine changes in the fresh relief of the landform in a short period (a few to several dozen months) and to calculate the components of the mass displacement balance within the kettle hole.
Figure 2 presents a sequence of steps in obtaining DEMs.This process consisted of the following stages: (1) fieldwork research, (2) video frames extraction, (3) point cloud generation, (4) filtering, scaling and rectification, (5) data export, ( 6) and DTM generation.The method of processing video recordings into a dense point cloud and exporting data using the described open-source software was inspired by the publication of Wróżyński et al. 9 .The authors applied a low-cost SfM method for obtaining information about microtopography using a smartphone and a camera in laboratory and terrain conditions.They presented a procedure largely adopted in this study and described in detail in the Methods section (see also Fig. 2).This general scheme is also consistent with the typical workflow of Carrivick et al. 1 (Fig. 3.1.,p. 38).

Methods
Fieldwork research.Data for the study were collected during a field session on Skeiðarársandur in southern Iceland in June 2022 and are freely accessible in the Zenodo repository 57,58 .Initially, 78 random kettle holes were selected to study the rate of aeolian sedimentation, and monitoring started in June 2021.The <Random Selection> option from <Research Tools> of vector layers in QGIS was used for this purpose (the <> signs indicate the name of an option/tool in a given program).Then, in 2022, clusters of kettle holes formed during several episodes of glacial floods (jökulhlaup) were selected in such a defined research area within two sandur levels.In the case of the first, older level, floods occurred during the maximum extent of Skeiðarárjökull glacier in the late 19th century and continued until the late 1930s 41,59 .The landforms are located in the proximal part  Íslands, https://dem.lmi.is/mapview/?application=DEM;40% of the layer opacity).The ISN93/Lambert 1993 coordinate system is used (the WGS84 coordinates notation).
an initial degree of plant colonisation.All tested kettle holes were described spatially by centroid coordinates in ISN93, WGS84, and UTM coordinate systems (Zenodo-KH_centroids_coord.zip 57).
Fieldwork began by installing 4-6 wooden stakes, 0.5 m long and 22 mm in diameter, around the edge of the kettle hole, measuring the height of the stakes (±1 mm) and the distance between them (±0.5 cm).The essential part of the work was video recording in the MOV format using a Nikon D3100 digital camera with a Complementary Metal Oxide Semiconductor (CMOS) sensor with a size of 14.2 million pixels and a focal length of 18 mm.Filming at a frequency of 24 frames per second consisted of circling the depression twice; the first time, the focus was on the stakes and the opposite edge of the landform, and the second time -on its interior.In this way, footage was obtained for 43 older landforms and 55 younger ones in 90 video files with a total size of approximately 50 GB 58 .
The average filming time was 253 s ± 135 s for the younger kettle holes and 193 s ± 76 s for the older level.It strictly depended on the landform size, i.e. twice its circumference, which had to be covered when filming the object.Another critical factor was the variety of terrain surfaces, affecting the comfort of the operator's route; large boulders, narrow ridges between depressions, or willow and birch trees slowed down the rate of circling the landform.Due to the asymmetric distribution of the data, all average parameters reported are the median and the interquartile range (IQR).Calculations are available in the spreadsheet Zenodo-Kettle-holes_param-eters_2022.xls 57 .
The stake position was not fitted into existing vertical and horizontal spatial reference systems.However, stakes were used to create local plane rectangular coordinate systems (values in meters) for each kettle hole to study changes over time.Moreover, the location of stakes contact with the ground was a network of checkpoints.Information about the stake parameters is available in the dataset in a ZIP file for each landform under the name Zenodo-Number_stakes.txt 57 .

Video frames extraction.
Further work was carried out using the software (Table 1).A Dell Precision 5530 laptop was used with an Intel ® Core ™ i9-8950HK CPU @ 2.90 GHz processor, 32GB RAM, an Intel ® UHD Graphics 630/NVIDIA Quadro P2000 graphics card, and SSD drive.
In VLC media player software, using <Scene filter> of the image tools, frames were extracted from the video at a frequency of 3-12 frames per second for stakes (to identify them in the point cloud) and one frame per second for the depression.Both sets of frames were then combined into one package.One frame had a size of 1,920 × 1,080 pixels and a resolution of 96 dpi in JPG format.
On average, 314 ± 24 frames per video were extracted, with a maximum of 334.With the given frame parameters, the maximum value resulted from the possibility of initiating the scene processing by another program, VisualSfM.Selected frames were included in the dataset (ZIP files for each kettle hole; Zenodo-Number_frames folder 57 ).point cloud generation.Sparse and dense models.In the next step, the VisualSFM program was used with the Clustering Views for Multi-view Stereo/Patch-based Multi-view Stereo Software (CMVS/PMVS) algorithm 9 .Changchang Wu developed VisualSFM, the fast-running application (multicore parallesim), for feature detection, feature matching, and bundle adjustment 60 .VisualSFM was, for example, used to monitor the position of a cliff in Ault in Northern France 61 .The author applied this program to create a point cloud based on 568 photographs and rectify the model to the Lambert-93 French official projection.Another application example would be monitoring the Super-Sauze landslide in the Southern French Alps using different surface reconstruction pipelines, including VisualSFM 62 .The free software PMVS was, in turn, used for dense reconstruction of the detritus dump located at Zijin Mine in Southeast China 63 or to obtain a camera calibration certification (internal and exterior orientation), for example, for studying soil erosion in Tuscany, Italy 64 .<Compute Missing Pairwise Matches>, the first option, the most time-consuming stage, required an average of 155.5 ± 38 minutes to process the optimal number of frames per video with the previously specified laptop parameters.In this way, sufficient overlap in content was identified in the extracted frames (<Pairwise matching>) to find identical points to obtain a three-dimensional effect (<Compute 3D Reconstruction>).The program uses the scale-invariant feature transform (SIFT) algorithm to recognition of the key features.A point cloud was generated, obtaining a sparse model (Fig. 4a), and then, via the <Run Dense Reconstruction> option, a dense model, i.e. a dense point cloud with a texture (Fig. 4b).Noise was manually removed from the cloud, leaving only points directly related to a given depression.The point cloud was exported in NVM and PLY formats.
Point clouds.Point clouds were ultimately generated for 85 kettle holes.Those videos that were not recorded correctly in terms of the assumptions of the SfM technique were excluded from further analysis.Another cause was insufficient or no visibility of the stakes.In case 48 of the video, all measurement stakes were visible.A minimum of two adjacent stakes was necessary to scale the point cloud.The average distance between stakes was 11.8 m ± 6.1 m for younger landforms and 10.8 m ± 5 m for older ones.Half of the distance measurements in the case of younger landforms covered 8-14 m (Fig. 5), and the second smaller maximum appeared in the 18-20 m (almost 10%).For older depressions, the maximum was in the range of 10-12 m (over 27%), and almost 90% of  the distances were in the range of 4-14 m.For larger landforms, the stakes were increased from 4 to 6. Hence, the histogram shows the type of saddle.Smaller distances between stakes made it easier to take measurements with a tape measure in stronger winds.As a result of the process, the raw point clouds had an average of 0.863 mln ±0.24 mln points for younger landforms and 0.865 mln ±0.19 mln points for older landforms.While the medians are similar, the data distribution differs (Fig. 6a).In the case of younger landforms, there are two dominants in the range of 0.7-0.8mln and 1.1-1.2mln points (23.5% of data each).It is mainly reflected in differences in vegetation cover and surfaces covered with fine-grained material.In the case of older landforms, almost 40% of the data is in the range of 0.8-0.9mln points, and the depressions are more homogeneous in terms of surface coverage, mainly by vegetation (mosses, lichens, blueberry shrubs, heather, etc.).
In the rescaled point clouds, where the noise has been removed, the average values are 0.743 mln ±0.14 mln points for younger landforms and 0.733 mln ±0.1 mln points for older landforms, respectively (Fig. 6b).Both groups of landforms are characterised by the maximum of 0.6-0.7 mln points; 40.4% in the case of younger kettle holes and 52.8% for older forms.There are 4,061 points per 1 m 2 of kettle hole, which averages approximately 10 points per area of 0.0025 m 2 , corresponding to the adopted resolution of the generated digital terrain models (0.05 × 0.05 m -one raster size).PLY files with scaled and noise-free point clouds were usually 15-40 MB and are available in the Zenodo dataset 57 -in ZIP files for each kettle hole.
Filtering, scaling and rectification.The next step was to scale the point cloud to the terrain dimensions obtained from field measurements.For this purpose, the <Transform: Scale, Normalize> tool from the <Normals, Curvatures and Orientation> filter in the MeshLab program was used 9 .Scaling was based on the scale factor, i.e. the ratio of the terrain distance to the distance read in the program using the <Measuring Tool>.Distance measurements were made when at least two adjacent stakes were visible in the point cloud, and stake height measurements were made when the entire stake was visible.The measurement was made ten times for each distance/height; the data was then averaged, and the scale factor was calculated.Training sessions were performed before the proper measurements were taken to familiarize with a given point cloud.All calculations were included in the dataset in spreadsheets (ZIP files for each kettle hole; Zenodo-ErrorsNumber.xls/.xlsxfiles 57 ).Stake names (distance markings) come from the first two letters of the colour, e.g.OR means an orange stake.
Kettle holes are immersed in a relatively flat surface, with an average inclination of approximately 0.6-1.6°,where the lowest point of the depression is the minimum value of the elevation of later DEMs.In other cases, i.e. filming on the slope, it is necessary to obtain information about the surface slope to know the height differences  Table 2. Name and format of files describing the content of the first part of the dataset at the Zenodo repository 57 .
between the bases of the stakes around a given landform.For this purpose, the slope can be determined based on other existing materials, or appropriate measurements can be made in the terrain.
Stakes distance and height measurements taken in the field allowed us to assess the accuracy of the data later used to create the DEM.It was the absolute value of the difference between the measured data and the data read (ten times) on the clouds before and after scaling (from now on referred to as the horizontal and vertical measurement error for simplicity).It corresponds to the values of root-mean-square error (RMSE).In the case of younger landforms, the average horizontal error (distance), taking into account the measurement error with a tape measure, was 0.032 m at IQR = 0.04 m, and the vertical error (height), taking into account the measurement error of the tool, was 0.004 m at IQR = 0.004 m.For older landforms, it was 0.022 m at IQR = 0.01 m and 0.002 m at IQR = 0.001 m, respectively.These error values corresponded to other reports when the handy-held, ground-based SfM technique was used 65,66 .
The vertical error remains relatively constant and low.It is because each 0.5 m long stake in the point cloud can be zoomed in as much as possible to read its height very clearly.The situation is different regarding the distance between stakes, which is about 20-30 times bigger.For both age groups, most differences in the horizontal error fall in the range up to 0.01 m -just over 25% of the measurements for younger landforms and almost half for older ones.Almost 85% are within the range of up to 0.06 m in the case of younger depressions, and almost 85% of the error values of older landforms are within the range of up to 0.03 m (Fig. 7a).
The comparison of the distances between stakes with the calculated horizontal errors shows direct relationships only for distances longer than 15 m (R = 0.65).After arranging the values of horizontal errors in ascending order, the average distances between stakes were calculated in each range of the calculated differences.This comparison indicates an 86-90% probability (Fig. 7b) that, according to this method, there is a statistically significant relationship (p = 0.05, n = 12 and n = 8) that up to 25 m of the distances between stakes horizontal errors should be smaller than 0.05 m on average.However, it is also visible that the horizontal errors rapidly increase above 25 meters of this distance.
In the case of kettle hole no.ZO17-2 within the younger sandur level, the point cloud was rectified to compare changes after the intervention in the relief of the landform bottom (the <GEOREF> option in MeshLab software).The model rectification process was based on reading local coordinates from characteristic points (stones), the so-called ground control points (GCP) from model ZO17-1, creating a GCP table and combining identical points of ZO17-2 model (the Technical Validation section).

Data export.
The scaled/rectified and denoised model in PLY format was imported to CloudCompare in the <Set Front View> option.We first see it as an RGB model (Fig. 8a).Information about the cloud density was obtained using the <Poisson Surface Reconstruction> plugin 67 .The main parameter, i.e. <octree depth >, was set to 10.Then, the <Scalar Fields> values obtained using the histogram (Fig. 8b) were applied to limit the Table 3.The content of ZIP data packages for each kettle hole in part I of the dataset at the Zenodo repository 57 .
Fig. 9 The relationship between the volume of kettle holes and the optimal resolution of DEMs with standard error bars.cloud coverage to the target density from <Min> 7 to <Max> obtained from the <Filter By Value> option.Additionally, the data was smoothed by the <Laplacian> function with default settings of 20 iterations and a smoothing factor of 0.2.The resulting new mesh was exported in the TXT format, obtaining, among others, information about the X (first field) and Y coordinates (third field) and the Z height (second field).Vector geometry tools were used to calculate the Cartesian surface area of the kettle hole and its perimeter.Its maximum and average depth were also calculated, and using the <Grid Volume> tool in the SAGA GIS program -the volume of the landform.

technical Validation
The most natural data validation obtained using the SfM technique is material from terrestrial laser scanning (TLS) 1 .It is due to creating non-selectively sampled data as a point cloud.The author does not have such data for this area or other data from classical photogrammetric techniques or differential Global Positioning System (dGPS) measurements.The DEMs available on the National Land Survey of Iceland website from the Airborne Laser Scanner (ALS) have a 2 × 2 m resolution.Since the data were not rectified to existing topographic coordinate systems, validation was based mainly on field measurements of the distances between stakes arranged around the landform and their height.On this basis, the vertical and horizontal errors of the point clouds were calculated, which is described in detail in the Methods -the Filtering, scaling and rectification section.The data set in the Zenodo repository includes the calculations (ZIP files for each kettle hole; Zenodo-ErrorsNumber.xls/.xlsxfiles 57 ).

Fig. 2
Fig. 2 Scheme of data processing using open-source software.

Fig. 4
Fig. 4 Point cloud obtained for the NZY15 kettle hole (an example) in VisualSFM and CMVS/PMVS: (a) sparse model with the visible camera position for selected video frames, (b) dense model -visible depression, boulders, vegetation and colourful stakes used for measurements.

Fig. 5
Fig. 5 Distances between stakes in the filmed kettle holes in June 2022.

Fig. 6
Fig. 6 Number of points within the point cloud of the filmed kettle holes in June 2022: (a) raw point cloud, (b) noise-free point cloud.

Fig. 7
Fig. 7 Statistical measures of kettle holes horizontal errors: (a) horizontal differences between the stakes distance measured during the fieldwork research and on point cloud in the software, (b) relationship between the mean stakes distances and mean horizontal errors (in groups of values arranged in ascending order) with standard error bars; p = 0.05, n = 12 for younger landforms and n = 8 for older kettle holes.

Fig. 8
Fig. 8 An example of a dense model of a NZY15 kettle hole in CloudCompare: (a) RGB model, (b) scalar fields model after smoothing with the Laplacian function and a point density histogram.
number, contact information, the title of the project under which the data was collected, description of the database structure, acknowledgements, link to part II by DOI number KH_centroids_coord.zipA package of SHP, TXT and CSV files with kettle holes centroid coordinates in various coordinate systems Kettle-holes_parameters_2022.xlsMain parameters of video files, point clouds and kettle holes Documentation_2021-05-X-ST10-00710.pdfProject documentation divided into sections: description of the purpose and context of the research, description of methods, database organization, acknowledgements, and references extracted from video in JPG format ErrorsNUMBER Horizontal and vertical error calculation in XLS and XLSX formats Kettle-hole_NUMBER 3D visualization of the kettle hole in PNG format -hypsometric tints and shaded relief NUMBER_DEM DEM in TXT format NUMBER_model-w-noise.1 Scaled point cloud of the kettle hole, without noise, in PLY format NUMBER_shapePoly SHP file of kettle hole shape NUMBER_stakes Stakes description in TXT format -height and distances between stakes from terrain measurements NUMBER_TIN_OPT-RES_w-stakes_blanked_norm DTM of kettle hole of the optimal resolution in GeoTIFF format NUMBER_TIN_0i05m_w-stakes_blanked_norm DTM of kettle hole of the 0.05 × 0.05 m resolution in GeoTIFF format

Fig. 10
Fig. 10 Control of methodological correctness of the kettle hole no.ZO17 for the 2022 DEM: (a) original landform relief (ZO17-1), (b) original kettle cover as an RGB model with the position of checkpoints (stakes) and GCPs, (c) landform relief after digging the bottom (ZO17-2), (d) kettle cover as an RGB model after bottom relief changes with the position of checkpoints and GCPs.The local horizontal and vertical coordinate systems are used (the coordinate notation in metres).

Fig. 11
Fig. 11 Control of methodological correctness -DEM of Difference between the original relief of the kettle hole (ZO17-1) and the modified relief (ZO17-2) and histogram, younger Skeiðarársandur level, June 2022.The local horizontal and vertical coordinate systems are used (the coordinate notation in metres).

Table 4 .
ZO17 stakes position in local horizontal and vertical coordinate systems (in metres).

Table 5 .
The generated text file was then used in geoinformation software like QGIS to process Local GCPs (pebbles) of the ZO17 kettle hole with modified relief with the residual error values used to rectify the point cloud to check methodological repeatability.

Table 6 .
Measurements (M) of the height (h) of stakes and the distance (d) between them on the rectified point cloud ZO17-2 in the MeshLab program and averaged error values (in metres).